A borehole clustering based method for lithological identification using logging data

In recent years, geoscientists have been employing machine learning techniques to automate lithological identification by integrating well logging data. However, in geologically complex regions, few have taken into consideration the differences between boreholes and the uneven distribution of lithology. Additionally, there has been limited effort to differentiate boreholes in the same region based on stratigraphic sequences when addressing these issues. We propose a workflow for machine learning-based automated lithological identification. Utilizing the Structural Deep Clustering Network (SDCN) algorithm for deep clustering, we differentiate logging sampling points with geological strata as the clustering scale, assigning each sampling point to its corresponding stratum. In order to obtain stratum information for each borehole, we have devised a Borehole Cluster Result Processing Layer. By segmenting logging data windows, we extract stratum information for each borehole, using the distinctiveness of borehole stratum information as the basis for borehole classification. Subsequently, we assess the impact of lithological classification on logging data for each borehole category using four machine learning methods: extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bidirectional long short-term memory (Bi-LSTM), and bidirectional gated recurrent unit (Bi-GRU). The experimental results indicate that, compared to the case where boreholes are not classified, the lithological classification performance for the majority of borehole categories has improved by 1% to 6%. However, there is also a category of boreholes where the classification performance is less than ideal due to the significant variability of diabase contained within the Paleogene strata in the electrical resistivity logging.